Customer segmentation using Clustering

In this project, I will analyze customer data from a grocery store to group customers into different categories based on their similarities. This process, called customer segmentation, helps to better understand and serve different types of customers. By identifying distinct groups, the business can tailor its products and services to meet the unique needs and preferences of each group, improving customer satisfaction and focusing on the most valuable customers

TABLE OF CONTENTS

• 1. IMPORTING LIBRARIES
• 2. LOADING DATA
• 3. DATA CLEANING
• 4. DATA PREPROCESSING
• 5. DIMENSIONALITY REDUCTION
• 6. CLUSTERING
• 7. EVALUATING MODELS
• 8. PROFILING
• 9. MY FINDINGS (Click here to directly go to the results)
• 10. END

pip install yellowbric

import numpy as np
import pandas as pd
import datetime
import matplotlib
import matplotlib.pyplot as plt
from matplotlib import colors
import seaborn as sns
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
from yellowbrick.cluster import KElbowVisualizer
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt, numpy as np
from mpl_toolkits.mplot3d import Axes3D
from sklearn.cluster import AgglomerativeClustering
from matplotlib.colors import ListedColormap
from sklearn import metrics
import warnings
import sys
if not sys.warnoptions:
    warnings.simplefilter("ignore")
np.random.seed(42)

LOADING DATA

#dataset
data = pd.read_csv("marketing_campaign.csv", sep="\t")
data.head()

	ID	Year_Birth	Education	Marital_Status	Income	Kidhome	Teenhome	Dt_Customer	Recency	MntWines	...	NumWebVisitsMonth	AcceptedCmp3	AcceptedCmp4	AcceptedCmp5	AcceptedCmp1	AcceptedCmp2	Complain	Z_CostContact	Z_Revenue	Response
0	5524	1957	Graduation	Single	58138.0	0	0	04-09-2012	58	635	...	7	0	0	0	0	0	0	3	11	1
1	2174	1954	Graduation	Single	46344.0	1	1	08-03-2014	38	11	...	5	0	0	0	0	0	0	3	11	0
2	4141	1965	Graduation	Together	71613.0	0	0	21-08-2013	26	426	...	4	0	0	0	0	0	0	3	11	0
3	6182	1984	Graduation	Together	26646.0	1	0	10-02-2014	26	11	...	6	0	0	0	0	0	0	3	11	0
4	5324	1981	PhD	Married	58293.0	1	0	19-01-2014	94	173	...	5	0	0	0	0	0	0	3	11	0

5 rows × 29 columns

Attributes

data.columns

Index(['ID', 'Year_Birth', 'Education', 'Marital_Status', 'Income', 'Kidhome',
       'Teenhome', 'Dt_Customer', 'Recency', 'MntWines', 'MntFruits',
       'MntMeatProducts', 'MntFishProducts', 'MntSweetProducts',
       'MntGoldProds', 'NumDealsPurchases', 'NumWebPurchases',
       'NumCatalogPurchases', 'NumStorePurchases', 'NumWebVisitsMonth',
       'AcceptedCmp3', 'AcceptedCmp4', 'AcceptedCmp5', 'AcceptedCmp1',
       'AcceptedCmp2', 'Complain', 'Z_CostContact', 'Z_Revenue', 'Response'],
      dtype='object')

data.shape

(2240, 29)

These are the features of this data set

people

• **ID**: Customer's unique identifier

• **Year_Birth**: Customer's birth year

• **Education**: Customer's education level

• **Marital_Status**: Customer's marital status

• **Income**: Customer's yearly household income

• **Kidhome**: Number of children in customer's household

• **Teenhome**: Number of teenagers in customer's household

• **Dt_Customer**: Date of customer's enrollment with the company

• **Recency**: Number of days since customer's last purchase

• **Complain**: 1 if the customer complained in the last 2 years, 0 otherwise

---

Products

• **MntWines**: Amount spent on wine in the last 2 years

• **MntFruits**: Amount spent on fruits in the last 2 years

• **MntMeatProducts**: Amount spent on meat in the last 2 years

• **MntFishProducts**: Amount spent on fish in the last 2 years

• **MntSweetProducts**: Amount spent on sweets in the last 2 years

• **MntGoldProds**: Amount spent on gold in the last 2 years

---

Promotion

• **NumDealsPurchases**: Number of purchases made with a discount

• **AcceptedCmp1**: 1 if customer accepted the offer in the 1st campaign, 0 otherwise

• **AcceptedCmp2**: 1 if customer accepted the offer in the 2nd campaign, 0 otherwise

• **AcceptedCmp3**: 1 if customer accepted the offer in the 3rd campaign, 0 otherwise

• **AcceptedCmp4**: 1 if customer accepted the offer in the 4th campaign, 0 otherwise

• **AcceptedCmp5**: 1 if customer accepted the offer in the 5th campaign, 0 otherwise

• **Response**: 1 if customer accepted the offer in the last campaign, 0 otherwise

---

Place

• **NumWebPurchases**: Number of purchases made through the company’s website

• **NumCatalogPurchases**: Number of purchases made using a catalogue

• **NumStorePurchases**: Number of purchases made directly in stores

• **NumWebVisitsMonth**: Number of visits to company’s website in the last month

---

DATA PREPROCESSING

data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2240 entries, 0 to 2239
Data columns (total 29 columns):
 #   Column               Non-Null Count  Dtype  
---  ------               --------------  -----  
 0   ID                   2240 non-null   int64  
 1   Year_Birth           2240 non-null   int64  
 2   Education            2240 non-null   object 
 3   Marital_Status       2240 non-null   object 
 4   Income               2216 non-null   float64
 5   Kidhome              2240 non-null   int64  
 6   Teenhome             2240 non-null   int64  
 7   Dt_Customer          2240 non-null   object 
 8   Recency              2240 non-null   int64  
 9   MntWines             2240 non-null   int64  
 10  MntFruits            2240 non-null   int64  
 11  MntMeatProducts      2240 non-null   int64  
 12  MntFishProducts      2240 non-null   int64  
 13  MntSweetProducts     2240 non-null   int64  
 14  MntGoldProds         2240 non-null   int64  
 15  NumDealsPurchases    2240 non-null   int64  
 16  NumWebPurchases      2240 non-null   int64  
 17  NumCatalogPurchases  2240 non-null   int64  
 18  NumStorePurchases    2240 non-null   int64  
 19  NumWebVisitsMonth    2240 non-null   int64  
 20  AcceptedCmp3         2240 non-null   int64  
 21  AcceptedCmp4         2240 non-null   int64  
 22  AcceptedCmp5         2240 non-null   int64  
 23  AcceptedCmp1         2240 non-null   int64  
 24  AcceptedCmp2         2240 non-null   int64  
 25  Complain             2240 non-null   int64  
 26  Z_CostContact        2240 non-null   int64  
 27  Z_Revenue            2240 non-null   int64  
 28  Response             2240 non-null   int64  
dtypes: float64(1), int64(25), object(3)
memory usage: 507.6+ KB

Data Cleaning Process

1. Handling Missing Values

• Problem:
  The Income column has missing values, and we need to handle them. One common approach is to drop rows that have missing data.

• Action:
  The dropna() function is used to remove rows with missing income values, reducing the total number of entries. The assumption here is that the missing income data is not recoverable or critical for modeling.

---

2. Date Parsing and Feature Creation

• Problem:
  Dt_Customer is the date when a customer joined the database, but it's not in the proper DateTime format.

• Action:
  Convert the Dt_Customer column into a DateTime object, then calculate the number of days each customer has been in the database relative to the most recent customer. This is stored in the new feature Customer_For.

---

3. Categorical Features

• Problem:
  Some columns, like Marital_Status and Education, are categorical (strings), and these need to be converted into numeric values for modeling.

• Action:
  The next step involves converting categorical variables into numerical values, which we'll do during preprocessing (Label Encoding).

Remove Rows with missing Income values

#To remove the NA values
data = data.dropna()
print("The total number of entries after removing the rows with missing values are:", len(data))

The total number of entries after removing the rows with missing values are: 2216

In the next step, I am going to create a feature out of "Dt_Customer" that indicates the number of days a customer is registered in the firm's database. However, in order to keep it simple, I am taking this value relative to the most recent customer in the record.

Thus to get the values I must check the newest and oldest recorded dates.

# Convert 'Dt_Customer' to datetime format, handling errors
data["Dt_Customer"] = pd.to_datetime(data["Dt_Customer"], errors='coerce', dayfirst=True)

data["Dt_Customer"] = pd.to_datetime(data["Dt_Customer"])
dates = []
for i in data["Dt_Customer"]:
    i = i.date()
    dates.append(i)  
#Dates of the newest and oldest recorded customer
print("The newest customer's enrolment date in therecords:",max(dates))
print("The oldest customer's enrolment date in the records:",min(dates))

The newest customer's enrolment date in therecords: 2014-06-29
The oldest customer's enrolment date in the records: 2012-07-30

Creating a feature ("Customer_For") of the number of days the customers started to shop in the store relative to the last recorded date

#Created a feature "Customer_For"
days = []
d1 = max(dates) #taking it to be the newest customer
for i in dates:
    delta = d1 - i
    days.append(delta)
data["Customer_For"] = days
data["Customer_For"] = pd.to_numeric(data["Customer_For"], errors="coerce")

Lets find the the unique values in the categorical features to get a clear idea of the data.

print("Total categories in the feature Marital_Status:\n", data["Marital_Status"].value_counts(), "\n")
print("Total categories in the feature Education:\n", data["Education"].value_counts())

Total categories in the feature Marital_Status:
 Marital_Status
Married     857
Together    573
Single      471
Divorced    232
Widow        76
Alone         3
Absurd        2
YOLO          2
Name: count, dtype: int64 

Total categories in the feature Education:
 Education
Graduation    1116
PhD            481
Master         365
2n Cycle       200
Basic           54
Name: count, dtype: int64

Feature Engineering

These transformations help simplify, clarify, and enrich the dataset for better analysis and predictive modeling.

1. Age of Customer

• Calculate the Age of the customer.
  This gives the customer's age as of the current year.

2. Total Spendings

• Create a new feature Spent by summing up the amounts spent on:
  • Wines, Fruits, Meat, Fish, Sweets, Gold.
    This represents the total expenditure across various categories.

3. Living Situation

• Derive Living_With from Marital_Status to represent the customer's living situation:
  • "Married" and "Together" → Partner
  • Other statuses (e.g., "Single", "Divorced") → Alone
    Indicates whether the customer lives alone or with a partner.

4. Children in the Household

• Create a Children feature by adding Kidhome and Teenhome, representing the total number of children in the household.
  Indicates how many children (kids and teenagers) live with the customer.

5. Family Size

• Calculate Family_Size by adding the Children and the number of adults (1 for "Alone", 2 for "Partner").
  Provides an overall count of family members in the household.

6. Parenthood Status

• Is_Parent is a binary feature:
  • 1 if the customer has children (i.e., Children > 0)
  • 0 otherwise
    Indicates whether the customer is a parent.

7. Education Categories

• Simplify the Education feature into three categories:
  • Undergraduate: "Basic", "2n Cycle"
  • Graduate: "Graduation"
  • Postgraduate: "Master", "PhD"
    Categorizes the customer's level of education.

8. Renaming Columns

• Rename spending-related columns for clarity:
  • "MntWines" → Wines, "MntFruits" → Fruits, etc.
    Makes the column names more intuitive.

9. Dropping Redundant Features

• Drop the following columns, as they are no longer needed for analysis:
  • Marital_Status, Dt_Customer, Z_CostContact, Z_Revenue, Year_Birth, ID
    Streamlines the dataset by removing irrelevant features.

---

#Feature Engineering
#Age of customer today 
data["Age"] = 2021-data["Year_Birth"]

#Total spendings on various items
data["Spent"] = data["MntWines"]+ data["MntFruits"]+ data["MntMeatProducts"]+ data["MntFishProducts"]+ data["MntSweetProducts"]+ data["MntGoldProds"]

#Deriving living situation by marital status"Alone"
data["Living_With"]=data["Marital_Status"].replace({"Married":"Partner", "Together":"Partner", "Absurd":"Alone", "Widow":"Alone", "YOLO":"Alone", "Divorced":"Alone", "Single":"Alone",})

#Feature indicating total children living in the household
data["Children"]=data["Kidhome"]+data["Teenhome"]

#Feature for total members in the householde
data["Family_Size"] = data["Living_With"].replace({"Alone": 1, "Partner":2})+ data["Children"]

#Feature pertaining parenthood
data["Is_Parent"] = np.where(data.Children> 0, 1, 0)

#Segmenting education levels in three groups
data["Education"]=data["Education"].replace({"Basic":"Undergraduate","2n Cycle":"Undergraduate", "Graduation":"Graduate", "Master":"Postgraduate", "PhD":"Postgraduate"})

#For clarity
data=data.rename(columns={"MntWines": "Wines","MntFruits":"Fruits","MntMeatProducts":"Meat","MntFishProducts":"Fish","MntSweetProducts":"Sweets","MntGoldProds":"Gold"})

#Dropping some of the redundant features
to_drop = ["Marital_Status", "Dt_Customer", "Z_CostContact", "Z_Revenue", "Year_Birth", "ID"]
data = data.drop(to_drop, axis=1)

Data set with New features

data.head()

data.describe()

	Income	Kidhome	Teenhome	Recency	Wines	Fruits	Meat	Fish	Sweets	Gold	...	AcceptedCmp1	AcceptedCmp2	Complain	Response	Customer_For	Age	Spent	Children	Family_Size	Is_Parent
count	2216.000000	2216.000000	2216.000000	2216.000000	2216.000000	2216.000000	2216.000000	2216.000000	2216.000000	2216.000000	...	2216.000000	2216.000000	2216.000000	2216.000000	2.216000e+03	2216.000000	2216.000000	2216.000000	2216.000000	2216.000000
mean	52247.251354	0.441787	0.505415	49.012635	305.091606	26.356047	166.995939	37.637635	27.028881	43.965253	...	0.064079	0.013538	0.009477	0.150271	3.054423e+16	52.179603	607.075361	0.947202	2.592509	0.714350
std	25173.076661	0.536896	0.544181	28.948352	337.327920	39.793917	224.283273	54.752082	41.072046	51.815414	...	0.244950	0.115588	0.096907	0.357417	1.749036e+16	11.985554	602.900476	0.749062	0.905722	0.451825
min	1730.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000e+00	25.000000	5.000000	0.000000	1.000000	0.000000
25%	35303.000000	0.000000	0.000000	24.000000	24.000000	2.000000	16.000000	3.000000	1.000000	9.000000	...	0.000000	0.000000	0.000000	0.000000	1.555200e+16	44.000000	69.000000	0.000000	2.000000	0.000000
50%	51381.500000	0.000000	0.000000	49.000000	174.500000	8.000000	68.000000	12.000000	8.000000	24.500000	...	0.000000	0.000000	0.000000	0.000000	3.071520e+16	51.000000	396.500000	1.000000	3.000000	1.000000
75%	68522.000000	1.000000	1.000000	74.000000	505.000000	33.000000	232.250000	50.000000	33.000000	56.000000	...	0.000000	0.000000	0.000000	0.000000	4.570560e+16	62.000000	1048.000000	1.000000	3.000000	1.000000
max	666666.000000	2.000000	2.000000	99.000000	1493.000000	199.000000	1725.000000	259.000000	262.000000	321.000000	...	1.000000	1.000000	1.000000	1.000000	6.039360e+16	128.000000	2525.000000	3.000000	5.000000	1.000000

8 rows × 28 columns

The above stats show some discrepancies in mean Income and Age and max Income and age.

Do note that max-age is 128 years, As I calculated the age wrt 2021 and the data is old.

I must take a look at the broader view of the data.

I will plot some of the selected features.

import seaborn as sns
import matplotlib.pyplot as plt
from matplotlib import colors

# Setting up color preferences with a different background color
sns.set(rc={"axes.facecolor": "#F4F4F9", "figure.facecolor": "#F4F4F9"})  # Set background to light gray

# Define a custom color palette for the plot
pallet = ["#1f77b4", "#ff7f0e", "#2ca02c", "#d62728", "#9467bd", "#8c564b"]
cmap = colors.ListedColormap(["#1f77b4", "#ff7f0e", "#2ca02c", "#d62728", "#9467bd", "#8c564b"])

# Plotting the selected features
To_Plot = ["Income", "Recency", "Customer_For", "Age", "Spent", "Is_Parent"]
print("Relative Plot Of Some Selected Features: A Data Subset")
plt.figure()

# Creating pairplot with the specified colors and the 'Is_Parent' hue
sns.pairplot(data[To_Plot], hue="Is_Parent", palette=["#1f77b4", "#ff7f0e"])  # Changed palette to two colors

# Show the plot
plt.show()

Relative Plot Of Some Selected Features: A Data Subset

<Figure size 800x550 with 0 Axes>

There are a few outliers in the Income and Age features. I will be deleting the outliers in the data.

#Dropping the outliers by setting a cap on Age and income. 
data = data[(data["Age"]<90)]
data = data[(data["Income"]<600000)]
print("The total number of data-points after removing the outliers are:", len(data))

The total number of data-points after removing the outliers are: 2212

let us look at the correlation amongst the features.

(Excluding the categorical attributes at this point)

# Select only numeric columns
numeric_data = data.select_dtypes(include=[np.number])

# Compute the correlation matrix
corrmat = numeric_data.corr()

# Plot the heatmap
plt.figure(figsize=(20, 20))
sns.heatmap(corrmat, annot=True, cmap='coolwarm', center=0)
plt.show()

The data is quite clean and the new features have been included.

Now let's do some preprocessing to perform clustering:

1. Label encoding the categorical features

   • Convert categorical features into numeric values to make them suitable for clustering algorithms.

2. Scaling the features using the standard scaler

   • Standardize the features to ensure they are on the same scale, which is important for clustering algorithms like K-Means that are sensitive to feature magnitudes.

3. Creating a subset dataframe for dimensionality reduction

   • Create a new dataframe by removing unnecessary columns, which will help with the dimensionality reduction process and improve the efficiency of the clustering.

#Get list of categorical variables
s = (data.dtypes == 'object')
object_cols = list(s[s].index)

print("Categorical variables in the dataset:", object_cols)

Categorical variables in the dataset: ['Education', 'Living_With']

#Label Encoding the object dtypes.
LE=LabelEncoder()
for i in object_cols:
    data[i]=data[[i]].apply(LE.fit_transform)
    
print("All features are now numerical")

All features are now numerical

#Creating a copy of data
ds = data.copy()
# creating a subset of dataframe by dropping the features on deals accepted and promotions
cols_del = ['AcceptedCmp3', 'AcceptedCmp4', 'AcceptedCmp5', 'AcceptedCmp1','AcceptedCmp2', 'Complain', 'Response']
ds = ds.drop(cols_del, axis=1)
#Scaling
scaler = StandardScaler()
scaler.fit(ds)
scaled_ds = pd.DataFrame(scaler.transform(ds),columns= ds.columns )
print("All features are now scaled")

All features are now scaled

Creating a copy of the data:
A copy of the original dataset is made using ds = data.copy() to avoid altering the original dataset.

Creating a subset of the dataframe:
Unnecessary columns related to promotions and complaints, such as 'AcceptedCmp1', 'AcceptedCmp2', 'Complain', and 'Response', are dropped using ds.drop(cols_del, axis=1) to focus on relevant features for clustering.

Scaling the features:
The StandardScaler is applied to standardize the features in the dataset.

• The scaler is fitted to the data (scaler.fit(ds)).
• The data is then transformed and stored in a new dataframe scaled_ds to ensure all features have the same scale.

#Scaled data to be used for reducing the dimensionality
print("Dataframe to be used for further modelling:")
scaled_ds.head()

Dataframe to be used for further modelling:

	Education	Income	Kidhome	Teenhome	Recency	Wines	Fruits	Meat	Fish	Sweets	...	NumCatalogPurchases	NumStorePurchases	NumWebVisitsMonth	Customer_For	Age	Spent	Living_With	Children	Family_Size	Is_Parent
0	-0.893586	0.287105	-0.822754	-0.929699	0.310353	0.977660	1.552041	1.690293	2.453472	1.483713	...	2.503607	-0.555814	0.692181	1.527721	1.018352	1.676245	-1.349603	-1.264598	-1.758359	-1.581139
1	-0.893586	-0.260882	1.040021	0.908097	-0.380813	-0.872618	-0.637461	-0.718230	-0.651004	-0.634019	...	-0.571340	-1.171160	-0.132545	-1.189011	1.274785	-0.963297	-1.349603	1.404572	0.449070	0.632456
2	-0.893586	0.913196	-0.822754	-0.929699	-0.795514	0.357935	0.570540	-0.178542	1.339513	-0.147184	...	-0.229679	1.290224	-0.544908	-0.206048	0.334530	0.280110	0.740959	-1.264598	-0.654644	-1.581139
3	-0.893586	-1.176114	1.040021	-0.929699	-0.795514	-0.872618	-0.561961	-0.655787	-0.504911	-0.585335	...	-0.913000	-0.555814	0.279818	-1.060584	-1.289547	-0.920135	0.740959	0.069987	0.449070	0.632456
4	0.571657	0.294307	1.040021	-0.929699	1.554453	-0.392257	0.419540	-0.218684	0.152508	-0.001133	...	0.111982	0.059532	-0.132545	-0.951915	-1.033114	-0.307562	0.740959	0.069987	0.449070	0.632456

5 rows × 23 columns

DIMENSIONALITY REDUCTION

Purpose of Dimensionality Reduction

The purpose of dimensionality reduction is to reduce the number of features (dimensions) in the dataset, making it easier to visualize, analyze, and cluster, while maintaining the key information.

In datasets with a large number of features, many of those features might be highly correlated (redundant), which makes it difficult to work with. By reducing the number of features, we can:

• Decrease computational complexity
• Increase model performance (e.g., less overfitting)
• Visualize data in fewer dimensions
• Remove noise or redundant information.

Principal Component Analysis (PCA) is a common technique for dimensionality reduction that transforms the data into a smaller set of uncorrelated variables, called principal components, which explain most of the variance in the data.

#Initiating PCA to reduce dimentions aka features to 3
pca = PCA(n_components=3)
pca.fit(scaled_ds)
PCA_ds = pd.DataFrame(pca.transform(scaled_ds), columns=(["col1","col2", "col3"]))
PCA_ds.describe().T

	count	mean	std	min	25%	50%	75%	max
col1	2212.0	4.497106e-17	2.878602	-5.978123	-2.539470	-0.781595	2.386380	7.452915
col2	2212.0	0.000000e+00	1.709469	-4.194757	-1.323932	-0.173716	1.234923	6.168185
col3	2212.0	4.015273e-17	1.231685	-3.625184	-0.853556	-0.051292	0.863841	6.746845

Here the original data is reduced to 3 dimensions, capturing the most important information with minimal loss

#A 3D Projection Of Data In The Reduced Dimension
x =PCA_ds["col1"]
y =PCA_ds["col2"]
z =PCA_ds["col3"]
#To plot
fig = plt.figure(figsize=(10,8))
ax = fig.add_subplot(111, projection="3d")
ax.scatter(x,y,z, c="maroon", marker="o" )
ax.set_title("A 3D Projection Of Data In The Reduced Dimension")
plt.show()

The plot visually shows how the data is distributed in the reduced 3D space, helping to understand the structure and spread of the data after dimensionality reduction.

CLUSTERING

In this part, I will perform clustering on the data. The goal is to group similar data points together, helping us understand the underlying patterns and structures within the dataset.

Here's the approach:

1. Use the Elbow Method:
   I will begin by applying the Elbow Method to determine the optimal number of clusters. This method evaluates the distortion score to show how well the data fits within clusters. When the score levels off, it indicates the ideal number of clusters.

2. Apply Agglomerative Clustering:
   Once the number of clusters is decided, I will use Agglomerative Clustering. This hierarchical method begins by treating each data point as its own cluster, then merges them step by step based on similarity until the desired number of clusters is formed.

3. Visualize the Clusters:
   After clustering, I will create a 3D scatter plot to visualize the clusters in the reduced 3D space (from PCA). This allows us to assess how the data points are grouped visually and check how well the clustering works.

The rationale behind this approach is to reduce data complexity (via PCA) and then group similar data points together (via clustering) to uncover meaningful patterns. Clustering helps to make sense of the data by identifying natural groupings, which can aid in understanding trends and making predictions.

# Quick examination of elbow method to find numbers of clusters to make.
print('Elbow Method to determine the number of clusters to be formed:')
Elbow_M = KElbowVisualizer(KMeans(), k=10)
Elbow_M.fit(PCA_ds)
Elbow_M.show()

Elbow Method to determine the number of clusters to be formed:

<Axes: title={'center': 'Distortion Score Elbow for KMeans Clustering'}, xlabel='k', ylabel='distortion score'>

The Elbow Method is used to determine the optimal number of clusters for the data.

This "elbow" point indicates the ideal number of clusters to use, ensuring that the model captures the data's underlying patterns without overfitting or underfitting.

This step is important because choosing the right number of clusters improves the effectiveness and interpretability of the clustering process.

In the next step, I am applying Agglomerative Clustering to the PCA-reduced dataset to form clusters.

Initiating and Fitting the Agglomerative Clustering Model:

Agglomerative clustering is a hierarchical clustering method that builds clusters by merging data points iteratively based on their similarity. It's particularly useful when the number of clusters is already determined (in this case, 4) and helps uncover groupings in the data without needing a pre-specified number of clusters at the start.

Process:

1. AgglomerativeClustering is initialized with n_clusters=4, specifying that the data should be divided into four clusters.
2. fit_predict() is applied to the PCA-reduced data (PCA_ds) to generate the cluster assignments.
3. The resulting cluster labels are stored in the PCA_ds dataframe under the "Clusters" column.
4. These cluster labels are then added to the original data dataframe as the "Clusters" column, allowing us to associate the cluster assignments with the original dataset.

#Initiating the Agglomerative Clustering model 
AC = AgglomerativeClustering(n_clusters=4)
# fit model and predict clusters
yhat_AC = AC.fit_predict(PCA_ds)
PCA_ds["Clusters"] = yhat_AC
#Adding the Clusters feature to the orignal dataframe.
data["Clusters"]= yhat_AC

#Plotting the clusters
fig = plt.figure(figsize=(10,8))
ax = plt.subplot(111, projection='3d', label="bla")
ax.scatter(x, y, z, s=40, c=PCA_ds["Clusters"], marker='o', cmap = cmap )
ax.set_title("The Plot Of The Clusters")
plt.show()

EVALUATION

Now, we evaluate the clusters formed by the Agglomerative Clustering model. Since clustering is an unsupervised learning technique, there are no labeled outcomes to evaluate the model directly. Instead, the evaluation focuses on exploring the patterns and characteristics of each cluster, and how they relate to different features in the dataset.

The goal is to understand how the data points are grouped, which can inform targeted actions, such as marketing strategies or customer segmentation. We will use Exploratory Data Analysis (EDA) to visually examine the cluster distribution and identify meaningful patterns within each group.

Key Steps in This Section:

1. Visualizing Cluster Distribution:

   • A countplot is used to display the distribution of data points across the different clusters. This helps us visually assess the balance of the clusters.

2. Cluster Profiling (Income vs Spending):

   • A scatterplot is created to visualize how clusters differ based on income and spending. The hue parameter is used to color-code the clusters, allowing us to visually compare cluster profiles.

3. Analyzing Product Preferences:

   • A combination of swarmplots and boxenplots is used to explore how spending differs between clusters. This reveals which cluster spends more on particular products.

4. Campaign Response Analysis:

   • A new feature, Total_Promos, is created by summing up accepted promotions across all campaigns. A countplot is then used to show how many promotions were accepted by customers in each cluster.

5. Purchasing Behavior:

   • Jointplots are used to examine the relationships between various purchasing behaviors (web, catalog, and store purchases) and spending within each cluster.

These steps will help us gain insights into the clusters and make data-driven decisions for targeted actions.

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# Visualizing the distribution of clusters
pal = ["#FF6347", "#4682B4", "#32CD32", "#FFD700"]
pl = sns.countplot(x=data["Clusters"], palette=pal)
pl.set_title("Distribution Of The Clusters")
plt.show()

Look like a good distribution

# Step 2: Profiling clusters based on income and spending with distinctive colors
pal = ["#FF6347", "#4682B4", "#32CD32", "#FFD700"]  # Distinctive Red, Blue, Green, and Yellow

pl = sns.scatterplot(data=data, x=data["Spent"], y=data["Income"], hue=data["Clusters"], palette=pal)
pl.set_title("Cluster's Profile Based On Income And Spending")
plt.legend()
plt.show()

Income vs spending plot shows the clusters pattern

• group 0: Average income higher spending
• group 1: lower income lower spending
• group 2: higher income higher spending
• group 3: higher income lower spending

Next, I’m going to check how the clusters are spread out across different product types like Wines, Fruits, Meat, Fish, Sweets, and Gold

plt.figure()
pl=sns.swarmplot(x=data["Clusters"], y=data["Spent"], color= "#CBEDDD", alpha=0.5 )
pl=sns.boxenplot(x=data["Clusters"], y=data["Spent"], palette=pal)
plt.show()

From the above plot, it can be clearly seen that cluster 2 is our biggest set of customers closely followed by cluster 0.

We can explore what each cluster is spending on for the targeted marketing strategies.

#Creating a feature to get a sum of accepted promotions 
data["Total_Promos"] = data["AcceptedCmp1"]+ data["AcceptedCmp2"]+ data["AcceptedCmp3"]+ data["AcceptedCmp4"]+ data["AcceptedCmp5"]
#Plotting count of total campaign accepted.
plt.figure()
pl = sns.countplot(x=data["Total_Promos"],hue=data["Clusters"], palette= pal)
pl.set_title("Count Of Promotion Accepted")
pl.set_xlabel("Number Of Total Accepted Promotions")
plt.show()

The response to the campaigns hasn’t been great so far, with only a few people participating overall. Interestingly, nobody has taken part in all 5 campaigns. It might be a good idea to design more targeted and better-thought-out campaigns to improve sales.

#Plotting the number of deals purchased
plt.figure()
pl=sns.boxenplot(y=data["NumDealsPurchases"],x=data["Clusters"], palette= pal)
pl.set_title("Number of Deals Purchased")
plt.show()

Cluster 0 has the highest number of deals purchased, represented by the tallest bar. This suggests that Cluster 0 had the most active purchasing activity compared to the other clusters.

Cluster 1 has the second-highest number of deals purchased, but the bar is significantly shorter than the bar for Cluster 0.

Cluster 2 has a relatively low number of deals purchased, with the bar being much shorter compared to Clusters 0 and 1.

Cluster 3 has the lowest number of deals purchased, with a small bar at the end of the chart.

so our best customers "group 2" is least interested in deals.. ok intresting!!

#for more details on the purchasing style 
Places =["NumWebPurchases", "NumCatalogPurchases", "NumStorePurchases",  "NumWebVisitsMonth"] 

for i in Places:
    plt.figure()
    sns.jointplot(x=data[i],y = data["Spent"],hue=data["Clusters"], palette= pal)
    plt.show()

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UNDERSTANDING CUSTOMER SEGMENTS

Now, the goal is to understand who makes up each cluster by analyzing the personal characteristics of the customers. By profiling these clusters, we can determine which groups include the retail store's most valuable customers and which groups require more focus from the marketing team.

This is achieved by visualizing various customer traits such as family size, education level, age, and parenting status against their spending behavior. Insights gained from this profiling will help in tailoring marketing strategies and understanding customer needs better.

Personal = [ "Kidhome","Teenhome","Customer_For", "Age", "Children", "Family_Size", "Is_Parent", "Education","Living_With"]

for i in Personal:
    plt.figure()
    sns.jointplot(x=data[i], y=data["Spent"], hue =data["Clusters"], kind="kde", palette=pal)
    plt.show()

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MY FINDINGS

Group 1: Family-Oriented, Higher Income, Higher Spending

• Key Traits: Older, family-oriented, educated, high income, spend more.
• Lifestyle: Likely to live with a partner and value family and health-related products.

Group 2: Independent, Lower Income, Lower Spending

• Key Traits: Younger, live alone, lower income, spend less.
• Lifestyle: Focus on individual needs and less likely to spend on non-essentials.

Group 3: Mixed Ages & Families, High Income, High Spending

• Key Traits: Diverse in age, mixed families, higher income, spend more.
• Lifestyle: Spend on a variety of goods and services, both personal and family-related.

Group 4: Young Families, Lower Education, Lower Spending

• Key Traits: Younger families with children, moderate income, spend less.
• Lifestyle: Focus on essentials for the family, budget-conscious.

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Spending Behavior

• Top Spenders:

  • Group 3: High income and high spending.
  • Group 1: High income, willing to spend on family needs.

• Lowest Spenders:

  • Group 2: Lower income, lower spending habits.

• Most Deal-Oriented Customers:

  • Group 1: Respond well to deals and promotions.
  • Group 4: Deal-sensitive, but spend less overall.

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My Recommendations for Sales & Marketing

For Group 1 - Family-Oriented, High Spend

• What to Sell: Family products, health-related items, senior discounts.
• How to Reach Them: Offer loyalty programs and exclusive deals.

For Group 2 - Young, Independent, Lower Spend

• What to Sell: Affordable lifestyle products, tech gadgets, entertainment.
• How to Reach Them: Focus on digital marketing and referral programs for discounts.

For Group 3 - High Income, High Spend

• What to Sell: Premium products, tech, family packages, travel.
• How to Reach Them: Use personalized offers, cross-sell, and bundling deals.

For Group 4 - Young Families, Budget-Conscious

• What to Sell: Essential products, family-focused deals, savings bundles.
• How to Reach Them: Use time-limited offers and cost-saving bundles.

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Summary

• Top Priority: Target Group 3 with premium products and personalized offers.
• Value Deals: Engage Group 1 with loyalty programs and exclusive discounts.
• Affordable Options: Focus on Group 2 with budget-friendly products and digital promotions.
• Family Essentials: Offer Group 4 savings bundles and time-limited promotions.

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